Image Data Processing

ABSTRACT

A digital image is processed to hide information, by generally inconspicuous adjustments thereof. These adjustments can define a pattern that extends across some or all of the image. Desirably, the pattern is adapted to the particular image being encoded, so as to better conceal the encoding.

RELATED APPLICATION DATA

This application is a continuation of copending application Ser. No.11/074,520, filed Mar. 7, 2005, which is a continuation of applicationSer. No. 10/113,398, filed Mar. 27, 2002 (now U.S. Pat. No. 7,068,811),which is a continuation of application Ser. No. 09/408,878, filed Sep.29, 1999 (now abandoned), which is a continuation of application Ser.No. 09/317,784, filed May 24, 1999 (now U.S. Pat. No. 6,072,888), whichis a continuation of application Ser. No. 09/074,632, filed May 7, 1998(now U.S. Pat. No. 5,930,377), which is a continuation of applicationSer. No. 08/969,072, filed Nov. 12, 1997 (now U.S. Pat. No. 5,809,160),which is a continuation of application Ser. No. 07/923,841, filed Jul.31, 1992 (now U.S. Pat. No. 5,721,788).

TECHNICAL FIELD

The present disclosure relates to image processing—to encode imageinformation with hidden supplemental information, and to decode suchsupplemental information from encoded images.

BACKGROUND AND SUMMARY

Various images in traditional print or photographic media are commonlydistributed to many users. Examples include the distribution of printsof paintings to the general public and photographs and film clips to andamong the media. Owners may wish to audit usage of their images in printand electronic media, and so require a method to analyze print, film anddigital images to determine if they were obtained directly from theowners or derived from their images. For example, the owner of an imagemay desire to limit access or use of the image. To monitor and enforcesuch a limitation, it would be beneficial to have a method of verifyingthat a subject image is copied or derived from the owner's image. Themethod of proof should be accurate and incapable of being circumvented.Further, the method should be able to detect unauthorized copies thathave been resized, rotated, cropped, or otherwise altered slightly.

In the computer field, digital signatures have been applied to non-imagedigital data in order to identify the origin of the data. For variousreasons these prior art digital signatures have not been applied todigital image data. One reason is that these prior art digitalsignatures are lost if the data to which they are applied are modified.Digital images are often modified each time they are printed, scanned,copied, or photographed due to unintentional “noise” created by themechanical reproduction equipment used. Further, it is often desired toresize, rotate, crop or otherwise intentionally modify the image.Accordingly, the existing digital signatures are unacceptable for usewith digital images.

In accordance with certain embodiments, the present disclosure detailsmethods and systems for embedding image signatures within visual images,applicable to digital representations as well as other media such asprint or film. The signatures can identify the source or ownership ofimages and distinguish between different copies of a single image.

In a particular embodiment described herein, a plurality of signaturepoints are selected that are positioned within an original image havingpixels with pixel values. The pixel values of the signature points areadjusted by an amount detectable by a digital scanner. The adjustedsignature points form a digital signature that is stored for futureidentification of subject images derived from the image.

The foregoing and other features will be more readily apparent from thefollowing detailed description, which proceeds with reference to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a computer system used in a preferred embodiment.

FIG. 2 is a sample digital image upon which the FIG. 1 embodiment isemployed.

FIG. 3 is a representation of a digital image in the form of an array ofpixels with pixel values.

FIG. 4 is graphical representation of pixel values showing relativeminima and maxima pixel values.

FIG. 5 is a digital subject image that is compared to the image of FIG.2 according to a preferred embodiment.

DETAILED DESCRIPTION

The following description details a method and system for embedding asignature into an original image to create a signed image. A preferredembodiment includes selecting a large number of candidate points in theoriginal image and selecting a number of signature points from among thecandidate points. The signature points are altered slightly to form thesignature. The signature points are stored for later use in auditing asubject image to determine whether the subject image is derived from thesigned image.

The signatures are encoded in the visible domain of the image and sobecome part of the image and cannot be detected or removed without priorknowledge of the signature. A key point is that while the changesmanifested by the signature are too slight to be visible to the humaneye, they are easily and consistently recognizable by a common digitalimage scanner, after which the signature is extracted, interpreted andverified by a software algorithm.

In contrast to prior art signature methods used on non-image data, thesignatures persist through significant image transformations thatpreserve the visible image but may completely change the digital data.The specific transforms allowed include resizing the image larger orsmaller, rotating the image, uniformly adjusting color, brightnessand/or contrast, and limited cropping. Significantly, the signaturespersist through the process of printing the image to paper or film andrescanning it into digital form.

Shown in FIG. 1 is a computer system 10 that is used to carry out anillustrative embodiment. The computer system 10 includes a computer 12having the usual complement of memory and logic circuits, a displaymonitor 14, a keyboard 16, and a mouse 18 or other pointing device. Thecomputer system also includes a digital scanner 20 that is used tocreate a digital image representative of an original image such as aphotograph or painting. Typically, delicate images, such as paintings,are converted to print or film before being scanned into digital form.In one embodiment a printer 22 is connected to the computer 12 to printdigital images output from the processor. In addition, digital imagescan be output in a data format to a storage medium 23 such as a floppydisk for displaying later at a remote site. Any digital display devicemay be used, such a common computer printer, X-Y plotter, or a displayscreen.

An example of the output of the scanner 20 to the computer 12 is adigital image 24 shown in FIG. 2. More accurately, the scanner outputsdata representative of the digital image and the computer causes thedigital image 24 to be displayed on the display monitor 14. As usedherein “digital image” refers to the digital data representative of thedigital image, the digital image displayed on the monitor or otherdisplay screen, and the digital image printed by the printer 22 or aremote printer.

The digital image 24 is depicted using numerous pixels 24 having variouspixel values. In the gray-scale image 24 the pixel values are luminancevalues representing a brightness level varying from black to white. In acolor image the pixels have color values and luminance values, both ofwhich being pixel values. The color values can include the values of anycomponents in a representation of the color by a vector. FIG. 3 showsdigital image 24A in the form of an array of pixels 26. Each pixel isassociated with one or more pixel values, which in the example shown inFIG. 3 are luminance values from 0 to 15.

The digital image 24 shown in FIG. 2 includes thousands of pixels. Thedigital image 24A represented in FIG. 3 includes 225 pixels. Thetechniques detailed herein preferably are used for images having pixelsnumbering in the millions. Therefore, the description herein isnecessarily a simplistic discussion.

According to a preferred embodiment, numerous candidate points arelocated within the original image. Signature points are selected fromamong the candidate points and are altered to form a signature. Thesignature is a pattern of any number of signature points. In a preferredembodiment, the signature is a binary number between 16 and 32 bits inlength. The signature points may be anywhere within an image, but arepreferably chosen to be as inconspicuous as possible. Preferably, thenumber of signature points is much greater than the number of bits in asignature. This allows the signature to be redundantly encoded in theimage. Using a 16 to 32 bit signature, 50-200 signature points arepreferable to obtain multiple signatures for the image.

A preferred embodiment locates candidate points by finding relativemaxima and minima, collectively referred to as extrema, in the image.The extrema represent local extremes of luminance or color. FIG. 4 showswhat is meant by relative extrema. FIG. 4 is a graphical representationof the pixel values of a small portion of a digital image. The verticalaxis of the graph shows pixel values while the horizontal axis showspixel positions along a single line of the digital image. Smallundulations in pixel values, indicated at 32, represent portions of thedigital image where only small changes in luminance or color occurbetween pixels. A relative maximum 34 represents a pixel that has thehighest pixel value for a given area of the image. Similarly, a relativeminimum 36 represents a pixel that has the lowest pixel value for agiven area of the image.

Relative extrema are preferred signature points for two major reasons.First, they are easily located by simple, well known processing. Second,they allow signature points to be encoded very inconspicuously.

One of the simplest methods to determine relative extrema is to use a“Difference of Averages” technique. This technique employs predeterminedneighborhoods around each pixel 26; a small neighborhood 28 and a largeneighborhood 30, as shown in FIGS. 2 and 3. In the present example theneighborhoods are square for simplicity, but a preferred embodimentemploys circular neighborhoods. The technique determines the differencebetween the average pixel value in the small neighborhood and theaverage pixel value of the large neighborhood. If the difference islarge compared to the difference for surrounding pixels then the firstpixel value is a relative maxima or minima.

Using the image of FIG. 3 as an example, the Difference of Averages forthe pixel 26A is determines as follows. The pixel values within the 3×3pixel small neighborhood 28A add up to 69; dividing by 9 pixels gives anaverage of 7.67. The pixel values within the 5×5 pixel largeneighborhood 30A add up to 219; dividing by 25 pixels gives an averageof 8.76 and a Difference of Averages of −1.09. Similarly, the average insmall neighborhood 28G is 10.0; the average in large neighborhood 30G is9.8; the Difference of Averages for pixel 26G is therefore 0.2. Similarcomputations on pixels 26B-26F produce the following table: 26A 26B 26C26D 26E 26F 26G Small Neighborhood 7.67 10.56 12.89 14.11 13.11 11.5610.0 Large Neighborhood 8.76 10.56 12.0 12.52 12.52 11.36 9.8 Differenceof −1.09 0.0 0.89 1.59 0.59 0.2 0.2 Averages

Based on pixels 26A-26G, there may be a relative maximum at pixel 26D,whose Difference of Averages of 1.59 is greater than the Difference ofAverages for the other examined pixels in the row. To determine whetherpixel 26D is a relative maximum rather than merely a small undulation,its Difference of Averages must be compared with the Difference ofAverages for the pixels surrounding it in a larger area.

Preferably, extrema within 10% of the image size of any side are notused as signature points. This protects against loss of signature pointscaused by the practice of cropping the border area of an image. It isalso preferable that relative extrema that are randomly and widelyspaced are used rather than those that appear in regular patterns.

Using the Difference of Averages technique or other known techniques, alarge number of extrema are obtained, the number depending on the pixeldensity and contrast of the image. Of the total number of extrema found,a preferred embodiment chooses 50 to 200 signature points. This may bedone manually by a user choosing with the keyboard 16, mouse 18, orother pointing device each signature point from among the extremadisplayed on the display monitor 14. The extrema may be displayed as adigital image with each point chosen by using the mouse or otherpointing device to point to a pixel or they may be displayed as a listof coordinates which are chosen by keyboard, mouse, or other pointingdevice. Alternatively, the computer 12 can be programmed to choosesignature points randomly or according to a preprogrammed pattern.

One bit of binary data is encoded in each signature point in the imageby adjusting the pixel values at and surrounding the point. The image ismodified by making a small, preferably 2%-110% positive or negativeadjustment in the pixel value at the exact signature point, to representa binary zero or one. The pixels surrounding each signature point, inapproximately a 5×5 to 10×10 grid, are preferably adjustedproportionally to ensure a continuous transition to the new value at thesignature point. A number of bits are encoded in the signature points toform a pattern which is the signature for the image.

In a preferred embodiment, the signature is a pattern of all of thesignature points. When auditing a subject image, if a statisticallysignificant number of potential signature points in the subject imagematch corresponding signature points in the signed image, then thesubject image is deemed to be derived from the signed image. Astatistically significant number is somewhat less than 100%, but enoughto be reasonably confident that the subject image was derived from thesigned image.

In an alternate embodiment, the signature is encoded using a redundantpattern that distributes it among the signature points in a manner thatcan be reliably retrieved using only a subset of the points. Oneembodiment simply encodes a predetermined number of exact duplicates ofthe signature. Other redundant representation methods, such as anerror-correcting code, may also be used.

In order to allow future auditing of images to determine whether theymatch the signed image, the signature is stored in a database in whichit is associated with the original image. The signature can be stored byassociating the bit value of each signature point together with x-ycoordinates of the signature point. The signature may be storedseparately or as part of the signed image. The signed image is thendistributed in digital form.

As discussed above, the signed image may be transformed and manipulatedto form a derived image. The derived image is derived from the signedimage by various transformations, such as resizing, rotating, adjustingcolor, brightness and/or contrast, cropping and converting to print orfilm. The derivation may take place in multiple steps or processes ormay simply be the copying of the signed image directly.

It is assumed that derivations of these images that an owner wishes totrack include only applications which substantially preserve theresolution and general quality of the image. While a size reduction by90%, a significant color alteration or distinct-pixel-value reductionmay destroy the signature, they also reduce the image's significance andvalue such that no auditing is desired.

In order to audit a subject image according to a preferred embodiment, auser identifies the original image of which the subject image issuspected of being a duplicate. For a print or film image, the subjectimage is scanned to create a digital image file. For a digital image, noscanning is necessary. The subject digital image is normalized usingtechniques as described below to the same size, and same overallbrightness, contrast and color profile as the unmodified original image.The subject image is analyzed by the method described below to extractthe signature, if present, and compare it to any signatures stored forthat image.

The normalization process involves a sequence of steps to undotransformations previously made to the subject image, to return it asclose as possible to the resolution and appearance of the originalimage. It is assumed that the subject image has been manipulated andtransformed as described above. To align the subject image with theoriginal image, a preferred embodiment chooses three or more points fromthe subject image which correspond to points in the original image. Thethree or more points of the subject image are aligned with thecorresponding points in the original image. The points of the subjectimage not selected are rotated and resized as necessary to accommodatethe alignment of the points selected.

For example, FIG. 5 shows a digital subject image 38 that is smallerthan the original image 24 shown in FIG. 2. To resize the subject image,a user points to three points such as the mouth 40B, ear 42B and eye 44Bof the subject image using the mouse 18 or other pointer. Since it isusually difficult to accurately point to a single pixel, the computerselects the nearest extrema to the pixel pointed to by the user. Theuser points to the mouth 40A, ear 42A, and eye 44A of the originalimage. The computer 12 resizes and rotates the subject image asnecessary to ensure that points 40B, 42B, and 44B are positioned withrespect to each other in the same way that points 40A, 42A, and 44A arepositioned with respect to each other in the original image. Theremaining pixels are repositioned in proportion to the repositioning ofpoints 40B, 42B and 44B. By aligning three points the entire subjectimage is aligned with the original image without having to align eachpixel independently.

After the subject image is aligned, the next step is to normalize thebrightness, contrast and/or color of the subject image. Normalizinginvolves adjusting pixel values of the subject image to match thevalue-distribution profile of the original image. This is accomplishedby a technique analogous to that used to align the subject image. Asubset of the pixels in the subject image are adjusted to equalcorresponding pixels in the original image. The pixels not in the subsetare adjusted in proportion to the adjustments made to the pixels in thesubset. The pixels of the subject image corresponding to the signaturepoints should not be among the pixels in the subset. Otherwise anysignature points in the subject image will be hidden from detection whenthey are adjusted to equal corresponding pixels in the original image.

In a preferred embodiment, the subset includes the brightest and darkestpixels of the subject image. These pixels are adjusted to have luminancevalues equal to the luminance values of corresponding pixels in theoriginal image. To ensure that any signature points can be detected, nosignature points should be selected during the signature embeddingprocess described above that are among the brightest and darkest pixelsof the original image. For example, one could use pixels among thebrightest and darkest 3% for the adjusting subset, after selectingsignature points among less than the brightest and darkest 5% to ensurethat there is no overlap.

When the subject image is fully normalized, it is preferably compared tothe original image. One way to compare images is to subtract one imagefrom the other. The result of the subtraction is a digital image thatincludes any signature points that were present in the subject image.These signature points, if any, are compared to the stored signaturepoints for the signed image. If the signature points do not match, thenthe subject image is not an image derived from the signed image, unlessthe subject image was changed substantially from the signed image.

In an alternative embodiment, the normalized subject image is compareddirectly with the signed image instead of subtracting the subject imagefrom the original image. This comparison involves subtracting thesubject image from the signed image. If there is little or no imageresulting from the subtraction, then the subject image equals to thesigned image, and therefore has been derived from the signed image.

In another alternate embodiment, instead of normalizing the entiresubject image, only a section of the subject image surrounding eachpotential signature point is normalized to be of the same generalresolution and appearance as a corresponding section of the originalimage. This is accomplished by selecting each potential signature pointof the subject image and selecting sections surrounding each potentialsignature point. The normalization of each selected section proceedsaccording to methods similar to those disclosed above for normalizingthe entire subject image.

Normalizing each selected section individually allows each potentialsignature point of the subject image to be compared directly with acorresponding signature point of the signed image. Preferably, anaverage is computed for each potential signature point by averaging thepixel value of the potential signature point with the pixel values of aplurality of pixels surrounding the potential signature point. Theaverage computed for each signature is compared directly with acorresponding signature point of the signed image.

While the methods of normalizing and extracting a signature from asubject image as described above are directed to luminance values,similar methods may be used for color values. Instead of or in additionto normalizing by altering luminance values, the color values of thesubject image can also be adjusted to equal corresponding color valuesin an original color image. However, it is not necessary to adjust colorvalues in order to encode a signature in or extract a signature from acolor image. Color images use pixels having pixel values that includeluminance values and color values. A digital signature can be encoded inany pixel values regardless of whether the pixel values are luminancevalues, color values, or any other type of pixel values. Luminancevalues are preferred because alterations may be made more easily toluminance values without the alterations being visible to the human eye.

From the foregoing it will be appreciated that, although specificembodiments of the invention have been described herein for purposes ofillustration, various modifications may be made without deviating fromthe spirit and scope of the invention. Accordingly, the invention is notlimited except as by the appended claims.

1. A signal with embedded supplemental data, the signal representingimage information, the signal being encoded in accordance with a givenencoding process, and selected samples of the signal being altered inaccordance with the supplemental data, the supplemental data comprisingplural bits, wherein said selection of samples is based, in part, onsuitability of said selected samples to be altered inconspicuously.
 2. Atangible, computer-readable storage medium having the signal of claim 1stored thereon.
 3. A signal with embedded supplemental data, the signalrepresenting image information, the signal being encoded in accordancewith a given encoding process, and selected samples of the signal beingaltered in accordance with the supplemental data, the supplemental datacomprising plural bits, wherein said selection is based, in part, onsuitability of said selected samples to persistently represent saidsupplemental data nothwithstanding transformations that change saidsignal.
 4. A tangible, computer-readable storage medium having thesignal of claim 3 stored thereon.
 5. A signal with embedded supplementaldata, the signal representing image information, the signal beingencoded in accordance with a given encoding process, and selectedsamples of the signal being altered in accordance with the supplementaldata, the supplemental data comprising plural bits, wherein said samplesare selected, in part, to facilitate location of corresponding samplesin a processed signal.
 6. A tangible, computer-readable storage mediumhaving the signal of claim 5 stored thereon.
 7. An encoding methodcomprising the acts: receiving input data, said data representing imageinformation; altering a subset of the input data to steganographicallyencode the image information with plural-bit embedded data; wherein themethod includes selecting the subset of input data for alterationthrough a computer-implemented process.
 8. The method of claim 7 whereinsaid computer-implemented process is based, in part, on a randomfunction.
 9. The method of claim 7 that comprises selecting data foralteration based on a preprogrammed pattern.
 10. The method of claim 7that comprises selecting data for alteration based on attributes of saidimage information, wherein the selection is image-dependent.
 11. Themethod of claim 7 wherein said selection results in alteration of pluralspaced-apart, non-contiguous portions of the image in the visibledomain.
 12. The method of claim 11 wherein said portions are notregularly arrayed across the image, but are randomly arrayed.
 13. Themethod of claim 7 wherein, in a first phase of operation, saidcomputer-implemented process generates an intermediate set of data, andin a following phase of operation, said computer-implemented processalters a subset of said intermediate set of data.
 14. The method ofclaim 7 wherein said selection causes the plural-bit embedded data to beencoded redundantly through the image information, permitting a completeset of said embedded data to be retrieved from versions of said imageinformation from which selected image information has been removed. 15.A method comprising: steganographically encoding input data withplural-bit supplemental data, the input data representing imageinformation; receiving derived data, said derived data corresponding tosaid encoded input data following a transformation process, saidtransformation process comprising one or more of the following:resizing, rotating, adjusting color, adjusting brightness, adjustingcontrast, cropping, converting to print, converting to film; andretrieving said plural-bit supplemental data from the derived data. 16.An image processing method, comprising the acts: receiving input data,said data representing image information; providing said data to ahidden data extraction processor; receiving from the processor a set ofembedded information recovered from said input data; and taking afurther action with use of said received embedded information.
 17. Themethod of claim 16 in which said input data has been altered since beingoriginally encoded with said embedded information, and the embeddedinformation received from the processor matches information originallyembedded.
 18. The method of claim 16 that further includes providing averification operation on the received embedded information.
 19. Themethod of claim 16 that further includes sensing a distortion functionto which the image represented by the input data has been subjected, andcompensating for at least some of said distortion.
 20. An imageprocessing method, comprising the acts: receiving input data that wasearlier encoded with hidden information, said input data representingimage information; processing said input data to restore it to a statethat more nearly approximates a state exhibited by the input data whenearlier encoded with said hidden information; and decoding the hiddeninformation from the processed image data.